Line and edge detection and enhancement

ABSTRACT

Embodiments of the present disclosure include one or more of a method, computing device, computer-readable medium, and system for statistical line and edge detection and/or enhancement. An example embodiment of the present disclosure may include a method that includes identifying a plurality of data values related to a first object defined by a first plurality of points within the volume, wherein the first object intersects a second object defined by a second plurality of points within the volume; calculating a statistical significance statistic related to the second object; interpolating a P-value related to the statistical significance statistic; and determining a significant P-value taken over the second object, wherein the significant P-value comprises a minimum P-value that provides a maximum negative log (P(statistical significance statistic)).

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 61/483081 filed May 6, 2011 entitled “STATISTICALLINE AND EDGE DETECTION AND ENHANCEMENT,” the entirety of which isincorporated by reference herein.

BACKGROUND

A Radon and Hough transform searches through an image for evidence ofedges/ridges by taking the integral over a line passing through theimage (e.g., for 2D). By testing many different lines through the image,the methods find which, if any, imaginary line has the strongest supportby comparing integrals. A method of edge detection that uses a Radontransform may take the integral, e.g., sum of values, along “allpossible lines,” and reports the maximum sum of values.

SUMMARY

Embodiments of the present disclosure include one or more of a method,computing device, computer-readable media and system for line and edgedetection and enhancement. An example embodiment of the presentdisclosure may include a method that includes identifying a plurality ofdata values related to a first object defined by a first plurality ofpoints within the volume, wherein the first object intersects a secondobject defined by a second plurality of points within the volume;calculating a statistical significance statistic related to the secondobject; interpolating a P-value related to the statistical significancestatistic; and determining a significant P-value taken over the secondobject, wherein the significant P-value comprises a minimum P-value thatprovides a maximum negative log (P(statistical significance statistic)).

BRIEF DESCRIPTION OF THE DRAWINGS

Implementations of various technologies will hereafter be described withreference to the accompanying drawings. It should be understood,however, that the accompanying drawings illustrate the variousimplementations described herein and are not meant to limit the scope ofvarious technologies described herein.

FIG. 1 shows a process flow diagram according to an embodiment of thepresent disclosure.

FIG. 2 shows a flowchart representing a method according to anembodiment of the present disclosure.

FIG. 3 shows example results of obtained upon applying an examplealgorithm with different parameter settings (e.g., line segment length).

FIG. 4a shows an example fault image prior to performing a methodaccording to an embodiment of the present disclosure.

FIG. 4b shows an example fault image after performing a method accordingto an embodiment of the present disclosure.

FIG. 5 shows a simplified illustration of a plane according to anembodiment of the present disclosure.

FIG. 6 shows a simplified illustration of a plane with sample valuesaccording to an embodiment of the present disclosure.

FIG. 7 shows a simplified illustration of a plane with example resultvalues according to an embodiment of the present disclosure.

FIGS. 8 and 9 show plots illustrating efficiency related to estimating avalue −log(P(z)) can be performed according to an embodiment of thepresent disclosure.

FIG. 10 illustrates a computer system into which implementations ofvarious technologies and techniques described herein.

DETAILED DESCRIPTION

FIG. 1 shows an example of a system 100 that includes various managementcomponents 110 to manage various aspects of a geologic environment 150.For example, the management components 110 may allow for direct orindirect management of sensing, drilling, injecting, extracting, etc.,with respect to the geologic environment 150. In turn, furtherinformation about the geologic environment 150 may become available asfeedback 160 (e.g., optionally as input to one or more of the managementcomponents 110).

In the example of FIG. 1, the management components 110 include aseismic data component 112, an information component 114, a processingcomponent 116, a simulation component 120, an attribute component 130,an analysis/visualization component 142 and a workflow component 144. Inoperation, seismic data and other information provided per thecomponents 112 and 114 may be input to the simulation component 120,optionally after processing via the processing component 116, which maybe configured to implement a Radon transform for processing seismicdata.

The simulation component 120 may process information to conform to oneor more attributes, for example, as specified by the attribute component130, which may be a library of attributes. Such processing may occurprior to input to the simulation component 120 (e.g., per the processingcomponent 116). Alternatively, or in addition to, the simulationcomponent 120 may perform operations on input information based on oneor more attributes specified by the attribute component 130. Asdescribed herein, the simulation component 120 may construct one or moremodels of the geologic environment 150, which may be relied on tosimulate behavior of the geologic environment 150 (e.g., responsive toone or more acts, whether natural or artificial). In the example of FIG.1, the analysis/visualization component 142 may allow for interactionwith a model or model-based results. Output from the simulationcomponent 120 may be input to one or more other workflows, as indicatedby a workflow component 144.

As described herein, the management components 110 may include featuresof a commercially available simulation framework such as the PETREL®seismic to simulation software framework (Schlumberger Limited, Houston,Tex.). The PETREL® framework provides components that allow foroptimization of exploration and development operations. The PETREL®framework includes seismic to simulation software components that canoutput information for use in increasing reservoir performance, forexample, by improving asset team productivity. Through use of such aframework, various professionals (e.g., geophysicists, geologists andreservoir engineers) can develop collaborative workflows and integrateoperations to streamline processes.

As described herein, the management components 110 may include featuresfor geology and geological modeling to generate high-resolutiongeological models of reservoir structure and stratigraphy (e.g.,classification and estimation, facies modeling, well correlation,surface imaging, structural and fault analysis, well path design, dataanalysis, fracture modeling, workflow editing, uncertainty andoptimization modeling, petrophysical modeling, etc.). Particularfeatures may allow for performance of rapid 2D and 3D seismicinterpretation, optionally for integration with geological andengineering tools (e.g., classification and estimation, well pathdesign, seismic interpretation, seismic attribute analysis, seismicsampling, seismic volume rendering, geobody extraction, domainconversion, etc.). As to reservoir engineering, for a generated model,one or more features may allow for simulation workflow to performstreamline simulation, reduce uncertainty and assist in future wellplanning (e.g., uncertainty analysis and optimization workflow, wellpath design, advanced gridding and upscaling, history match analysis,etc.). The management components 110 may include features for drillingworkflows including well path design, drilling visualization, andreal-time model updates (e.g., via real-time data links).

As described herein, various aspects of the management components 110may include add-ons or plug-ins that operate according to specificationsof a framework environment. For example, a commercially availableframework environment marketed as the OCEAN® framework environment(Schlumberger Limited) allows for seamless integration of add-ons (orplug-ins) into a PETREL® framework workflow. The OCEAN® frameworkenvironment leverages .NET® tools (Microsoft Corporation, Redmond,Washington) and offers stable, user-friendly interfaces for efficientdevelopment. As described herein, various components may be implementedas add-ons (or plug-ins) that conform to and operate according tospecifications of a framework environment (e.g., according toapplication programming interface (API) specifications, etc.). Varioustechnologies described herein may be optionally implemented ascomponents in an attribute library.

In the field of seismic analysis, aspects of a geologic environment maybe defined as attributes. In general, seismic attributes help tocondition conventional amplitude seismic data for improved structuralinterpretation tasks, such as determining the exact location oflithological terminations and helping isolate hidden seismicstratigraphic features of a geologic environment. Attribute analysis canbe helpful in defining a trap in exploration, or delineating andcharacterizing a reservoir at the appraisal and development phase. Anattribute generation process (e.g., in the PETREL® framework or otherframework) may rely on a library of various seismic attributes (e.g.,for display and use with seismic interpretation and reservoircharacterization workflows). At times, a need or desire may exist forgeneration of attributes on the fly for rapid analysis. At other times,attribute generation may occur as a background process (e.g., a lowerpriority thread in a multithreaded computing environment), which canallow for one or more foreground processes (e.g., to enable a user tocontinue using various components).

Attributes can help extract valuable information from seismic and otherdata, for example, by providing details related to lithologicalvariations of a geologic environment (e.g., an environment that includesone or more reservoirs).

In the oil and gas industry, existing approaches for detection offaults, fractures and estimation of possible stress in layers close tothe surface may include analysis of attributes based on local dip anglefor the surface, attributes based on local azimuth angle for the surfaceand attributes based on curvature of a single surface.

Detecting and extracting edges in seismic volumes corresponding tofaults may be a difficult problem to handle in an automated fashion. Aworkflow may include taking a seismic volume, applying an edge detectionor indicator attribute to the seismic, and running an edge enhancementor extractor method on the edge volume. PETREL® seismic to simulationsoftware (Schlumberger Limited, Houston, Texas) (referred to herein as“PETREL®” software), may include seismic attributes such as “chaos” and“variance” that can be used to detect edges. Certain versions of PETREL®software may also support “ant-tracking” as an enhancement method, asdescribed in U.S. Pat. No. 7,203,342. Furthermore, certain versions ofPETREL® software may support windowed radon transform as described inand U.S. patent application Ser. No. 12/940,469.

An example embodiment of the present disclosure may use a statisticaltest to detect one or more edges present in a visualization of seismicdata. For example, an embodiment may include a point centered approachthat involves using a statistical test, rather than an integral (e.g.,testing statistically for evidence of a line passing through a pluralityof points in a 2D image). Such statistical tests may involvedetermination of a statistical significance statistic (“SSS”), such as az-statistic, for example. A statistical approach differs from a methodthat uses Radon and Hough transforms, because such Radon and Houghtransforms are integral-based and consider lines going through an imageor sub-image.

An example method can also be applied to searching for planes in3D—however 3D applications may incur a higher computational cost than 2Dapplications. To limit the associated computational cost, a window maybe defined around a point of interest, and a statistical test may beused with respect data within the window to determine if there isevidence of a line segment that passes through the point of interest.

According to an example embodiment, a statistical test may be used tominimize the effect of outlier values upon results. For example, anon-parametric statistical test, such as a “Wilcoxon-Mann-Whitney ranksum test” or a “sign test” may be used.

In another example embodiment, statistical tests that do not use ranksor signs. For example, according to an example implementation, anynon-parametric statistical test may be used to compare a relative orderof values. In some cases, a non-parametric test might not make certainassumptions about the distribution of values, and therefore may be morerobust in certain cases than a parametric test against extreme values.

According to another example embodiment, the statistical test mayinclude a parametric test, such as, without limitation, a “t-test.”However, in such an embodiment, calculating −log(P(SSS)) (i.e., negativelog(P(SSS))) may become more difficult and more expensive since thenumber of degrees of freedom may depend on the settings of the radiusetc. In certain embodiments where a parametric test is used, normaldistributed data might be assumed.

FIG. 2 shows an example embodiment of a method 200 for processing aseismic volume. The method 200 may include at least one or more of thefollowing:

-   -   For a plurality of samples in a seismic volume (e.g., a portion        of samples, or all samples):        -   Block 205: Identify a first object defined by a first            plurality of points within the volume (e.g., a plane,            volume, or sector of data values around the samples)        -   Block 210: For a second object that intersects with the            first object, wherein the second object is defined a second            plurality of points (e.g., a line or plane through the first            object) do the following:            -   Block 210 a: Calculate an SSS, wherein an SSS may                include a z-statistic calculated from using a                statistical test (e.g., a parametric statistical test                (e.g., a t-test) or a non-parametric statistical test                (e.g., a U-test or a sign test);            -   210 b: Interpolate a P-value related to the SSS (e.g.,                where in the P-value is determined using the following:                −log(P(SSS))) (see section below titled “Calculating                P-values efficiently”);            -   210 c: Repeat blocks 210 a-b for at least a portion of                all lines and or planes that intersect the first object.    -   220: Output a significant P-value taken over the second shape        (e.g., the significant P-value may be a minimum P-value that        provides a maximum −log(P(SSS))).

The result of the foregoing method may be used to obtain a directsignificance metric as output, where P(SSS) may be the one-tailedP-value of the SSS. In an example embodiment, SSS may represent a ranksum standard normal approximation, and P-value can be approximated forextreme values of the SSS by linear interpolation. For example,sqrt(−log(P(SSS))) may be linear for SSS below 0 (one-tailed P-value,Here sqrt( ) is the square root function). If the input data is adjustedso that what we are looking for may provide a small SSS, e.g. in thenegative tail of the normal distribution, then we can use a predefinedtable and linear interpolation to quickly determine sqrt(−log(P(SSS))).Output values may then be squared to obtain results.

A volume version of the foregoing example method may be computingresource-intensive. However, it may be possible to approximate thevolume method by using a predetermined number of vertical planes goingthrough a point (e.g., one to four), and taking the max −log(P(SSS))from lines in those planes. For N planes the method may take N timesmore than one plane. Iterated scans alternating between the vertical andhorizontal planes may approximate the volume method.

As described herein, an example embodiment includes a method forperforming at least a portion of an edge detection/extraction workflow.Such an embodiment may serve as a complementary method or an alternatemethod with respect to existing edge detection methods, including,without limitation, ant-tracking technology.

Example Applications—Edge Detection/Enhancement

Since the example methods disclosed herein may be used to highlightedges, such methods can be used directly as an edgedetection/enhancement method with respect to seismic data. In an exampleembodiment, the methods described herein can be applied several timeshorizontally and vertically in sequence to obtain a pseudo-3D method.

Example Applications—Edge Volume Blending

Furthermore, since the output represents the evidence of the presence ofan edge in a volume, the output can be used to decide the weights formixing volumes. For example, if volume A indicates an edge at a givenlocation (i.e., i, j, k) in the input seismic (e.g., P-value 0.01), butvolume B does not have strong indications of an edge in the sameposition (P-value 0.25), then volumes A and B can be mixed at position(i, j, k) by taking the inverse of the P-values 0.01 and 0.25. Theweights for A(i, j, k) and B(i, j, k) then become: 1/0.01=100 and1/0.25=4. Normalizing the weights so that they sum to 1 produces:

C(i,j,k)=0.9615A(i,j,k)+0.0385B(i,j,k)

Example Edge Enhancement

An example embodiment of the methods described herein may be implementedas a volume attribute, as shown in FIG. 3. For example, an exampleembodiment may be implemented in seismic-to-simulation workflowsoftware, such as PETREL® software. Output from a method according tothe present disclosure may include different parameter settings (linesegment length). FIG. 3 shows a plurality of images 300 a-f, whichillustrate a possible progression in line-detection improvements thatmay be obtained when an example method is applied to a horizontal plane(time-slice).

A method according to the present disclosure may identify variouselements of an image that look like a line, whether such elements appearweak or strong, in an unprocessed image. By running a plurality ofiterations, it may be possible to make stronger and/or longer linesstand out further. It may also be possible to filter on certainsignificance values, thereby leaving lines with especially strong proofin the data. With incremental scanning for lines alternating betweenvertical and horizontal scans, it may be possible to extract faults,including, without limitation, large faults. In an example method, whenlooking for faults a user may wish to smooth the input so that thelarger faults stand out more as compared to smaller features.

FIGS. 4a-4b show examples of faults indicated by repeated runs on asmoothed volume. FIG. 4a shows a fault image 410 prior to running amethod according to an embodiment of the present disclosure, and FIG. 4bshows a fault image 420 after performing a method according to anembodiment of the present disclosure. As shown in FIGS. 4a -4 b,variance may be used as an edge indicator attribute.

Calculating a Z-Statistic from Ranks

In an example embodiment, the SSS may be a z-statistic. Given a searchfor lines through a plane (2D version) with radius r, the z-statisticcan be calculated by sorting the values in the plane (e.g., all thevalues in the plane), and the sorted values may be used to calculateranks. The values of a line passing through the plane may then becompared to other values in the plane (e.g., at least a portion of allvalues in the plane). From the ranks, the rank sum statistic may becalculated, which again may be used to calculate the z-approximation.

A sector of the plane around each line may be used for calculating theranks. This can be favorable in the case of crossing lines, sincecomparing against values from the whole plane may include the valuesfrom another line, which might skew the background distribution.

FIG. 5 shows a simplified illustration of a plane 500, with a linepassing through the center (the line passes through the points markedwith an “x”). The shaded boxes represent samples that may be dropped, inorder to define a sector around the line. In another embodiment, sectorscan also include a parameter as a number of degrees taken from thecentre of the plane.

Calculating a Z-Statistic from Signs

Another way of calculating a z-statistic may include comparing neighborpoints and using the signs of the comparisons to determine thesignificance. For a line passing through a plane, we can compare eachpoint on the line to neighboring points (e.g., perpendicular to theline) and collect the signs of the comparisons. From the signs we cancalculate the sign test statistic, and the corresponding z-statistic. Aparameter to the method may be how many neighbor points to compareagainst. This way of measuring the evidence of a line may be similar tothe sector-based approach described earlier, but uses fewer comparisons.This example method can potentially detect edges that are fading fromstrong to weak better than the ranks method, since comparisons are localin the plane.

Here is an example that involves comparing neighbor cells: each diagonalsample may be compared against neighbors in the plane, and the resultsmay be recorded as the number of times the diagonal cell is larger thanthe neighbor cells. This statistic may be expected to follow a binomialdistribution with probability 0.5. FIG. 6a shows a simplifiedillustration of a plane 600 with sample values according to an exampleembodiment. FIG. 7 shows a simplified illustration of a plane 700 withresult values according to an example embodiment. In the example shownin FIGS. 6 and 7, the total of the diagonal values are larger than theneighbor values in 19 out of 22 comparisons. Using the normalapproximation to the normal distribution, the expected value is 11, andthe estimated standard deviation is 2.345. Thus we obtain a z-statisticvalue 3.411 (P-value 3.24e-4).

Calculating P-Values

Calculating P-values can be an expensive operation in certainsituations, and may include taking an integral over a probabilitydistribution function of the test statistic. A property of the standardnormal distribution may be utilized to estimate the P-value of thez-statistic. In an example method, for low values of z, say less than−1, the expression √{square root over (−log(P(z)))} may be almostlinear. Adapting the input values to give low values for z for the typeof edges that are of interest, the value −log(P(z)) can be calculatedbased on a lookup table and linear interpolation with high accuracy.

FIGS. 8 and 9 show plots illustrating the point above about howestimating the value −log(P(z)) can be done efficiently. As can be seenfrom FIGS. 8 and 9, the maximal estimation error occurs around z=0,which may not be the values that are interesting with respect to anembodiment of the present disclosure. In an embodiment, extreme valuesof z may be interesting (z<−2 at least). The plot shown in FIG. 9represents maximal estimation error with table increment 1 for a linearinterpolation. Using smaller table increments may reveal higher accuracyon the estimates.

Computer System for Oilfield Application System

FIG. 10 illustrates a computer system 1000 into which implementations ofvarious technologies and techniques described herein may be implemented.In one implementation, computing system 1000 may be a conventionaldesktop or a server computer, but it should be noted that other computersystem configurations may be used.

The computing system 1000 may include a central processing unit (CPU)1021, a system memory 1022 and a system bus 1023 that couples varioussystem components including the system memory 1022 to the CPU 1021.Although one CPU is illustrated in FIG. 10, it should be understood thatin some implementations the computing system 1000 may include more thanone CPU. The system bus 1023 may be any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. By wayof example, and not limitation, such architectures include IndustryStandard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus,Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA)local bus, and Peripheral Component Interconnect (PCI) bus also known asMezzanine bus. The system memory 1022 may include a read-only memory(ROM) 1024 and a random access memory (RAM) 1025. A basic input/outputsystem (BIOS) 1026, containing the basic routines that help transferinformation between elements within the computing system 1000, such asduring start-up, may be stored in the ROM 1024.

The computing system 1000 may further include a hard disk drive 1027 forreading from and writing to a hard disk, a magnetic disk drive 1028 forreading from and writing to a removable magnetic disk 1029, and anoptical disk drive 1030 for reading from and writing to a removableoptical disk 1031, such as a CD ROM or other optical media. The harddisk drive 1027, the magnetic disk drive 1028 and the optical disk drive1030 may be connected to the system bus 1023 by a hard disk driveinterface 1032, a magnetic disk drive interface 1033, and an opticaldrive interface 1034, respectively. The drives and their associatedcomputer-readable media may provide nonvolatile storage ofcomputer-readable instructions, data structures, program modules andother data for the computing system 1000.

Although the computing system 1000 is described herein as having a harddisk, a removable magnetic disk 1029 and a removable optical disk 1031,it should be appreciated by those skilled in the art that the computingsystem 1000 may also include other types of computer-readable media thatmay be accessed by a computer. For example, such computer-readable mediamay include computer storage media and communication media. Computerstorage media may include volatile and non-volatile, and removable andnon-removable media implemented in any method or technology for storageof information, such as computer-readable instructions, data structures,program modules or other data. Computer storage media may furtherinclude RAM, ROM, erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), flashmemory or other solid state memory technology, CD-ROM, digital versatiledisks (DVD), or other optical storage, magnetic cassettes, magnetictape, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store the desired information andwhich can be accessed by the computing system 1000. Communication mediamay embody computer readable instructions, data structures, programmodules or other data in a modulated data signal, such as a carrier waveor other transport mechanism and may include any information deliverymedia. By way of example, and not limitation, communication media mayinclude wired media such as a wired network or direct-wired connection,and wireless media such as acoustic, RF, infrared and other wirelessmedia. Combinations of any of the above may also be included within thescope of computer readable media.

A number of program modules may be stored on the hard disk 1027,magnetic disk 1029, optical disk 1031, ROM 1024 or RAM 1025, includingan operating system 1035, one or more application programs 1036, programdata 1038 and a database system 1055. The operating system 1035 may beany suitable operating system that may control the operation of anetworked personal or server computer, such as Windows® XP, Mac OS® X,Unix-variants (e.g., Linux® and BSD®), and the like. In oneimplementation, plug-in manager 420, oilfield application 410, theplug-in quality application and the plug-in distribution applicationdescribed in FIGS. 4-9 in the paragraphs above may be stored asapplication programs 1036 in FIG. 10.

A user may enter commands and information into the computing system 1000through input devices such as a keyboard 1040 and pointing device 1042.Other input devices may include a microphone, joystick, game pad,satellite dish, scanner or the like. These and other input devices maybe connected to the CPU 1021 through a serial port interface 1046coupled to system bus 1023, but may be connected by other interfaces,such as a parallel port, game port or a universal serial bus (USB). Amonitor 1047 or other type of display device may also be connected tosystem bus 1023 via an interface, such as a video adapter 1048. Inaddition to the monitor 1047, the computing system 1000 may furtherinclude other peripheral output devices such as speakers and printers.

Further, the computing system 1000 may operate in a networkedenvironment using logical connections to one or more remote computers1049. The logical connections may be any connection that is commonplacein offices, enterprise-wide computer networks, intranets, and theInternet, such as local area network (LAN) 1051 and a wide area network(WAN) 1052. The remote computers 1049 may each include applicationprograms 1036 similar to that as described above. In one implementation,the plug-in quality application (i.e., performing method 500) stored inplug-in quality center 460 may be stored as application programs 1036 insystem memory 1022. Similarly, the plug-in distribution application(i.e., performing method 600) stored in plug-in distribution center 470may be stored as application programs 1036 in remote computers 1049.

When using a LAN networking environment, the computing system 1000 maybe connected to the local network 1051 through a network interface oradapter 1053. When used in a WAN networking environment, the computingsystem 1000 may include a modem 1054, wireless router or other means forestablishing communication over a wide area network 1052, such as theInternet. The modem 1054, which may be internal or external, may beconnected to the system bus 1023 via the serial port interface 1046. Ina networked environment, program modules depicted relative to thecomputing system 1000, or portions thereof, may be stored in a remotememory storage device 1050. It will be appreciated that the networkconnections shown are and other means of establishing a communicationslink between the computers may be used.

It should be understood that the various technologies described hereinmay be implemented in connection with hardware, software or acombination of both. Thus, various technologies, or certain aspects orportions thereof, may take the form of program code (i.e., instructions)embodied in tangible media, such as floppy diskettes, CD-ROMs, harddrives, or any other machine-readable storage medium wherein, when theprogram code is loaded into and executed by a machine, such as acomputer, the machine becomes an apparatus for practicing the varioustechnologies. In the case of program code execution on programmablecomputers, the computing device may include a processor, a storagemedium readable by the processor (including volatile and non-volatilememory and/or storage elements), at least one input device and at leastone output device. One or more programs that may implement or utilizethe various technologies described herein may use an applicationprogramming interface (API), reusable controls and the like. Suchprograms may be implemented in a high level procedural or objectoriented programming language to communicate with a computer system.However, the program(s) may be implemented in assembly or machinelanguage, if desired. In any case, the language may be a compiled orinterpreted language, and combined with hardware implementations.

While the foregoing is directed to implementations of varioustechnologies described herein, other and further implementations may bedevised without departing from the basic scope thereof, which may bedetermined by the claims that follow. As an example, embodiments of thepresent disclosure may also be directed at the market for ant-trackingapplications. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims. Although various methods,devices, systems, etc., have been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as examples of forms ofimplementing the claimed methods, devices, systems, etc.

What is claimed is:
 1. A method for processing seismic data, comprising:identifying a plurality of data values related to a first object definedby a first plurality of points within the volume, wherein the firstobject intersects a second object defined by a second plurality ofpoints within the volume; calculating a statistical significancestatistic related to the second object; interpolating a P-value relatedto the statistical significance statistic; and determining a significantP-value taken over the second object, wherein the significant P-valuecomprises a minimum P-value that provides a maximum negativelog(P(statistical significance statistic)).
 2. The method of claim 1,further comprising using the significant P-value to identify an edgerepresented by the seismic data.
 3. The method of claim 1, wherein theseismic data comprises a first set of seismic data, and furthercomprising using the significant P-value to determine a weight formixing a first volume represented by the seismic data with a secondvolume represented by a second set of seismic data.
 4. The method ofclaim 1, wherein the statistical significance statistic is determinedusing at least one of a parametric test and a non-parametric test. 5.The method of claim 1, wherein the statistical significance statistic isdetermined using at least one of a Wilcoxon-Mann-Whitney rank sum test,a t-test, a U-test and a Sign test.
 6. The method of claim 1, whereinthe statistical significance statistic comprises a z-statistic.
 7. Themethod of claim 1, wherein the maximum negative log(P(statisticalsignificance statistic)) is calculated based on a lookup table andlinear interpolation.
 8. One or more computer-readable media forprocessing seismic data, the computer-readable media comprisingcomputer-executable instructions to instruct a computing device toperform a process, the process comprising: identifying a plurality ofdata values related to a first object defined by a first plurality ofpoints within the volume, wherein the first object intersects a secondobject defined by a second plurality of points within the volume;calculating a statistical significance statistic related to the secondobject; interpolating a P-value related to the statistical significancestatistic; and determining a significant P-value taken over the secondobject, wherein the significant P-value comprises a minimum P-value thatprovides a maximum negative log(P(statistical significance statistic)).9. The computer-readable media of claim 8, wherein the process furthercomprises using the significant P-value to identify an edge representedby the seismic data.
 10. The computer-readable media of claim 8, whereinthe seismic data comprises a first set of seismic data, and furthercomprising using the significant P-value to determine a weight formixing a first volume represented by the seismic data with a secondvolume represented by a second set of seismic data.
 11. Thecomputer-readable media of claim 8, wherein the statistical significancestatistic is determined using at least one of a parametric test and anon-parametric test.
 12. The computer-readable media of claim 8, whereinthe statistical significance statistic is determined using at least oneof a Wilcoxon-Mann-Whitney rank sum test, a t-test, a U-test and a Signtest.
 13. The computer-readable media of claim 8, wherein thestatistical significance statistic comprises a z-statistic.
 14. Thecomputer-readable media of claim 8, wherein the maximum negativelog(P(statistical significance statistic)) is calculated based on alookup table and linear interpolation.
 15. A system for processingseismic data, comprising: a processor; a memory; a storage medium; aplurality of computer-executable instructions residing in the storagemedium to instruct the processor to perform a process, the processcomprising: identifying a plurality of data values related to a firstobject defined by a first plurality of points within the volume, whereinthe first object intersects a second object defined by a secondplurality of points within the volume; calculating a statisticalsignificance statistic related to the second object; interpolating aP-value related to the statistical significance statistic; anddetermining a significant P-value taken over the second object, whereinthe significant P-value comprises a minimum P-value that provides amaximum negative log(P(statistical significance statistic)).
 16. Thecomputer-readable media of claim 15, wherein the process furthercomprises using the significant P-value to identify an edge representedby the seismic data.
 17. The computer-readable media of claim 15,wherein the seismic data comprises a first set of seismic data, andfurther comprising using the significant P-value to determine a weightfor mixing a first volume represented by the seismic data with a secondvolume represented by a second set of seismic data.
 18. Thecomputer-readable media of claim 15, wherein the statisticalsignificance statistic is determined using at least one of a parametrictest and a non-parametric test.
 19. The computer-readable media of claim15, wherein the statistical significance statistic comprises az-statistic.
 20. The computer-readable media of claim 15, wherein themaximum negative log(P(statistical significance statistic)) iscalculated based on a lookup table and linear interpolation.